{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Introduction to Probability and Statistics\r\n",
    "## Assignment\r\n",
    "\r\n",
    "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87</td>\n",
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       "      <th>1</th>\n",
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       "      <td>21.6</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>156</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>85</td>\n",
       "      <td>141</td>\n",
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       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>198</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89</td>\n",
       "      <td>206</td>\n",
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       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>192</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ]
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     "metadata": {},
     "execution_count": 13
    }
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   "metadata": {}
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  {
   "cell_type": "markdown",
   "source": [
    "\r\n",
    "In this dataset, columns as the following:\r\n",
    "* Age and sex are self-explanatory\r\n",
    "* BMI is body mass index\r\n",
    "* BP is average blood pressure\r\n",
    "* S1 through S6 are different blood measurements\r\n",
    "* Y is the qualitative measure of disease progression over one year\r\n",
    "\r\n",
    "Let's study this dataset using methods of probability and statistics.\r\n",
    "\r\n",
    "### Task 1: Compute mean values and variance for all values"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "df.describe()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              AGE         SEX         BMI          BP          S1          S2  \\\n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
       "\n",
       "               S3          S4          S5          S6           Y  \n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  \n",
       "mean    49.788462    4.070249    4.641411   91.260181  152.133484  \n",
       "std     12.934202    1.290450    0.522391   11.496335   77.093005  \n",
       "min     22.000000    2.000000    3.258100   58.000000   25.000000  \n",
       "25%     40.250000    3.000000    4.276700   83.250000   87.000000  \n",
       "50%     48.000000    4.000000    4.620050   91.000000  140.500000  \n",
       "75%     57.750000    5.000000    4.997200   98.000000  211.500000  \n",
       "max     99.000000    9.090000    6.107000  124.000000  346.000000  "
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
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       "      <td>442.000000</td>\n",
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       "      <td>442.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>48.518100</td>\n",
       "      <td>1.468326</td>\n",
       "      <td>26.375792</td>\n",
       "      <td>94.647014</td>\n",
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       "      <td>49.788462</td>\n",
       "      <td>4.070249</td>\n",
       "      <td>4.641411</td>\n",
       "      <td>91.260181</td>\n",
       "      <td>152.133484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.109028</td>\n",
       "      <td>0.499561</td>\n",
       "      <td>4.418122</td>\n",
       "      <td>13.831283</td>\n",
       "      <td>34.608052</td>\n",
       "      <td>30.413081</td>\n",
       "      <td>12.934202</td>\n",
       "      <td>1.290450</td>\n",
       "      <td>0.522391</td>\n",
       "      <td>11.496335</td>\n",
       "      <td>77.093005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.258100</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>25.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>96.050000</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.276700</td>\n",
       "      <td>83.250000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.620050</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>140.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.275000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>209.750000</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.997200</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>211.500000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>242.400000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>6.107000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>346.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Another way\r\n",
    "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                AGE       SEX        BMI          BP           S1          S2  \\\n",
       "Mean       48.51810  1.468326  26.375792   94.647014   189.140271  115.439140   \n",
       "Variance  171.84661  0.249561  19.519798  191.304401  1197.717241  924.955494   \n",
       "\n",
       "                  S3        S4        S5          S6            Y  \n",
       "Mean       49.788462  4.070249  4.641411   91.260181   152.133484  \n",
       "Variance  167.293585  1.665261  0.272892  132.165712  5943.331348  "
      ],
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       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
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       "      <th>Mean</th>\n",
       "      <td>48.51810</td>\n",
       "      <td>1.468326</td>\n",
       "      <td>26.375792</td>\n",
       "      <td>94.647014</td>\n",
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       "      <td>91.260181</td>\n",
       "      <td>152.133484</td>\n",
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       "      <th>Variance</th>\n",
       "      <td>171.84661</td>\n",
       "      <td>0.249561</td>\n",
       "      <td>19.519798</td>\n",
       "      <td>191.304401</td>\n",
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       "      <td>167.293585</td>\n",
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     },
     "metadata": {},
     "execution_count": 8
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   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
    "df.mean()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "AGE     48.518100\n",
       "SEX      1.468326\n",
       "BMI     26.375792\n",
       "BP      94.647014\n",
       "S1     189.140271\n",
       "S2     115.439140\n",
       "S3      49.788462\n",
       "S4       4.070249\n",
       "S5       4.641411\n",
       "S6      91.260181\n",
       "Y      152.133484\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "for col in ['BMI','BP','Y']:\r\n",
    "    df.boxplot(column=col,by='SEX')\r\n",
    "plt.show()"
   ],
   "outputs": [
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     "output_type": "display_data",
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"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "for col in ['AGE','SEX','BMI','Y']:\r\n",
    "    df[col].hist()\r\n",
    "    plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
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"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Conclusions:\r\n",
    "* Age - normal\r\n",
    "* Sex - uniform\r\n",
    "* BMI, Y - hard to tell"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
    "\r\n",
    "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "df.corr()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          AGE       SEX       BMI        BP        S1        S2        S3  \\\n",
       "AGE  1.000000  0.173737  0.185085  0.335428  0.260061  0.219243 -0.075181   \n",
       "SEX  0.173737  1.000000  0.088161  0.241010  0.035277  0.142637 -0.379090   \n",
       "BMI  0.185085  0.088161  1.000000  0.395411  0.249777  0.261170 -0.366811   \n",
       "BP   0.335428  0.241010  0.395411  1.000000  0.242464  0.185548 -0.178762   \n",
       "S1   0.260061  0.035277  0.249777  0.242464  1.000000  0.896663  0.051519   \n",
       "S2   0.219243  0.142637  0.261170  0.185548  0.896663  1.000000 -0.196455   \n",
       "S3  -0.075181 -0.379090 -0.366811 -0.178762  0.051519 -0.196455  1.000000   \n",
       "S4   0.203841  0.332115  0.413807  0.257650  0.542207  0.659817 -0.738493   \n",
       "S5   0.270774  0.149916  0.446157  0.393480  0.515503  0.318357 -0.398577   \n",
       "S6   0.301731  0.208133  0.388680  0.390430  0.325717  0.290600 -0.273697   \n",
       "Y    0.187889  0.043062  0.586450  0.441482  0.212022  0.174054 -0.394789   \n",
       "\n",
       "           S4        S5        S6         Y  \n",
       "AGE  0.203841  0.270774  0.301731  0.187889  \n",
       "SEX  0.332115  0.149916  0.208133  0.043062  \n",
       "BMI  0.413807  0.446157  0.388680  0.586450  \n",
       "BP   0.257650  0.393480  0.390430  0.441482  \n",
       "S1   0.542207  0.515503  0.325717  0.212022  \n",
       "S2   0.659817  0.318357  0.290600  0.174054  \n",
       "S3  -0.738493 -0.398577 -0.273697 -0.394789  \n",
       "S4   1.000000  0.617859  0.417212  0.430453  \n",
       "S5   0.617859  1.000000  0.464669  0.565883  \n",
       "S6   0.417212  0.464669  1.000000  0.382483  \n",
       "Y    0.430453  0.565883  0.382483  1.000000  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173737</td>\n",
       "      <td>0.185085</td>\n",
       "      <td>0.335428</td>\n",
       "      <td>0.260061</td>\n",
       "      <td>0.219243</td>\n",
       "      <td>-0.075181</td>\n",
       "      <td>0.203841</td>\n",
       "      <td>0.270774</td>\n",
       "      <td>0.301731</td>\n",
       "      <td>0.187889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>0.173737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088161</td>\n",
       "      <td>0.241010</td>\n",
       "      <td>0.035277</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>-0.379090</td>\n",
       "      <td>0.332115</td>\n",
       "      <td>0.149916</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>0.043062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.185085</td>\n",
       "      <td>0.088161</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>0.249777</td>\n",
       "      <td>0.261170</td>\n",
       "      <td>-0.366811</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.446157</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.586450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BP</th>\n",
       "      <td>0.335428</td>\n",
       "      <td>0.241010</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242464</td>\n",
       "      <td>0.185548</td>\n",
       "      <td>-0.178762</td>\n",
       "      <td>0.257650</td>\n",
       "      <td>0.393480</td>\n",
       "      <td>0.390430</td>\n",
       "      <td>0.441482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1</th>\n",
       "      <td>0.260061</td>\n",
       "      <td>0.035277</td>\n",
       "      <td>0.249777</td>\n",
       "      <td>0.242464</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896663</td>\n",
       "      <td>0.051519</td>\n",
       "      <td>0.542207</td>\n",
       "      <td>0.515503</td>\n",
       "      <td>0.325717</td>\n",
       "      <td>0.212022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.219243</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>0.261170</td>\n",
       "      <td>0.185548</td>\n",
       "      <td>0.896663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196455</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>0.318357</td>\n",
       "      <td>0.290600</td>\n",
       "      <td>0.174054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>-0.075181</td>\n",
       "      <td>-0.379090</td>\n",
       "      <td>-0.366811</td>\n",
       "      <td>-0.178762</td>\n",
       "      <td>0.051519</td>\n",
       "      <td>-0.196455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.738493</td>\n",
       "      <td>-0.398577</td>\n",
       "      <td>-0.273697</td>\n",
       "      <td>-0.394789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>0.203841</td>\n",
       "      <td>0.332115</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.257650</td>\n",
       "      <td>0.542207</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>-0.738493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.617859</td>\n",
       "      <td>0.417212</td>\n",
       "      <td>0.430453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>0.270774</td>\n",
       "      <td>0.149916</td>\n",
       "      <td>0.446157</td>\n",
       "      <td>0.393480</td>\n",
       "      <td>0.515503</td>\n",
       "      <td>0.318357</td>\n",
       "      <td>-0.398577</td>\n",
       "      <td>0.617859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464669</td>\n",
       "      <td>0.565883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>0.301731</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.390430</td>\n",
       "      <td>0.325717</td>\n",
       "      <td>0.290600</td>\n",
       "      <td>-0.273697</td>\n",
       "      <td>0.417212</td>\n",
       "      <td>0.464669</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.382483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.187889</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.586450</td>\n",
       "      <td>0.441482</td>\n",
       "      <td>0.212022</td>\n",
       "      <td>0.174054</td>\n",
       "      <td>-0.394789</td>\n",
       "      <td>0.430453</td>\n",
       "      <td>0.565883</td>\n",
       "      <td>0.382483</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Conclusion:\r\n",
    "* The strongest correlation of Y is BMI and S5 (blood sugar). This sounds reasonable."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
    "    ax[i].scatter(df['Y'],df[n])\r\n",
    "    ax[i].set_title(n)\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "from scipy.stats import ttest_ind\r\n",
    "\r\n",
    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "T-value = -0.90\n",
      "P-value: 0.3674449793083975\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Conclusion: p-value close to 0 (typically, below 0.05) would indicate high confidence in our hypothesis. In our case, there is no strong evidence that sex affects progression of diabetes."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  }
 ],
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   "name": "python",
   "version": "3.8.8",
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    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.8 64-bit (conda)"
  },
  "interpreter": {
   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
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 },
 "nbformat": 4,
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